The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

Alessandro Brusa (), Fenil Panalal Shethia, Boris Petrone, Nicolò Cavina, Davide Moro, Giovanni Galasso and Ioannis Kitsopanidis
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Alessandro Brusa: Department of Industrial Engineering, University of Bologna, 40136 Bologna, Italy
Fenil Panalal Shethia: Department of Industrial Engineering, University of Bologna, 40136 Bologna, Italy
Boris Petrone: Department of Industrial Engineering, University of Bologna, 40136 Bologna, Italy
Nicolò Cavina: Department of Industrial Engineering, University of Bologna, 40136 Bologna, Italy
Davide Moro: Department of Industrial Engineering, University of Bologna, 40136 Bologna, Italy
Giovanni Galasso: Ferrari S.p.A., 41053 Maranello, Italy
Ioannis Kitsopanidis: Ferrari S.p.A., 41053 Maranello, Italy

Energies, 2024, vol. 17, issue 21, 1-26

Abstract: The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

Keywords: machine learning; neural networks; engine modeling; effect superposition; analytical functions; generalization; fault prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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